An edge-cloud collaborative framework for the online optimisation and control of milling deformation
针对薄壁件铣削变形问题，提出一种边云协同框架，云端用改进NSGA-II算法预设参数，边缘层根据扰动动态优化，显著提升效率并减少变形。
Milling deformation is a prevalent quality issue in thin-walled parts due to their low stiffness and high material removal rates. However, conventional milling uses fixed cutting parameters that cannot respond to dynamic disturbances, leading to low efficiency and uncontrolled deformation. To address the above issues, this paper proposes a novel multi-access edge computing (MEC) enabled edge-cloud collaborative framework for the online optimisation and control of milling deformation for thin-walled parts, which significantly improves efficiency and reduces deformation in thin-walled parts. The framework predefines a set of theoretical milling parameters with an improved NSGA-II algorithm at the cloud level. Then, during the milling process, the MEC level dynamically optimises the theoretical milling parameters according to the changes in milling deformation caused by milling disturbances. Finally, the feasibility and effectiveness of the proposed approach are demonstrated through the milling process of a typical thin-walled part.